Pioneering the next generation of medical diagnostics with precision engineering and vibrant innovation
Our research team are expert in creating & testing AI models that can classify medical images.
We use those AI models to create medical devices that redefine patient care standards.
class FatCubeEngine:
def analyze(scan):
// Loading Neural Weights...
result = model.predict(scan)
return result.confidence
157M parameters
Multi-Scale Feature Extraction with Spatial and Channel Attention mechanisms for granular detail capture.
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Dual-Branch Multi-Scale Convolution with Progressive Depth for complex pattern recognition.
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Rotation-Invariant Quad-Model Architecture (0°, 90°, 180°, 270°) for consistent diagnostic output.
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Core Processing Block repeated 42 times with 5 parallel branches for high-throughput processing.
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Five Clinically-Specialised Expert Branches with Progressive Multi-Level Fusion for targeted analysis.
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Dual-Backbone Feature Extraction (CNN + Global Vision Transformer) for global and local context.
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Comparing every patch of the image to every other patch, at once. Not just the adjacent patch.
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